The project aims to build AI methods that design cyclic peptides optimizing binding affinity, permeability, stability, and synthetic feasibility to enable oral delivery and replace parenteral biologics
Job Summary
The project aims to build AI methods that design cyclic peptides optimizing binding affinity, permeability, stability, and synthetic feasibility to enable oral delivery and replace parenteral biologics.
The PhD student will be integrated in both Stockholm University and AstraZeneca environments, benefiting from interdisciplinary collaboration and access to industrial datasets and expertise.
AstraZeneca is a global, science-driven biopharmaceutical company dedicated to turning ideas into life-changing medicines with a diverse and international workforce.
Matching Summary
The project aims to build AI methods that design cyclic peptides optimizing binding affinity, permeability, stability, and synthetic feasibility to enable oral delivery and replace parenteral biologics.
Skills & Requirements
Must-have
Molecular AI for peptide design
Proficiency in Python programming
Experience with modern deep learning models
Oral bioavailability and permeability modeling
Collaborative scientific communication skills
Nice-to-have
Peptide or macrocycle modeling experience
Familiarity with protein language models
Multi-objective optimization in generative workflows